Template-Free Symbolic Performance Modeling of Analog Circuits via Canonical-Form Functions and Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{McConaghy:2009:ieeeCADICS,
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author = "Trent McConaghy and Georges G. E. Gielen",
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title = "Template-Free Symbolic Performance Modeling of Analog
Circuits via Canonical-Form Functions and Genetic
Programming",
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journal = "IEEE Transactions on Computer-Aided Design of
Integrated Circuits and Systems",
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year = "2009",
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volume = "28",
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pages = "1162--1175",
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number = "8",
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month = aug,
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keywords = "genetic algorithms, genetic programming, SPICE,
analogue circuits CAFFEINE, SPICE simulation data,
analog circuits, arbitrary nonlinear circuits,
canonical-form functions, compact interpretable
symbolic performance models, kriging, neural networks,
posynomials, product-of-sum layers, splines,
sum-of-product layers, support vector machines,
template-free symbolic performance modeling",
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ISSN = "0278-0070",
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URL = "http://trent.st/content/2009-TCAD-caffeine_scale.pdf",
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DOI = "doi:10.1109/TCAD.2009.2021034",
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size = "14 pages",
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abstract = "This paper presents CAFFEINE, a method to
automatically generate compact interpretable symbolic
performance models of analog circuits with no prior
specification of an equation template. CAFFEINE uses
SPICE simulation data to model arbitrary nonlinear
circuits and circuit characteristics. CAFFEINE
expressions are canonical-form functions:
product-of-sum layers alternating with sum-of-product
layers, as defined by a grammar. Multiobjective genetic
programming trades off error with model complexity. On
test problems, CAFFEINE models demonstrate lower
prediction error than posynomials, splines, neural
networks, kriging, and support vector machines. This
paper also demonstrates techniques to scale CAFFEINE to
larger problems.",
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notes = "Also known as \cite{5166638}",
- }
Genetic Programming entries for
Trent McConaghy
Georges G E Gielen
Citations